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Detecting Deepfakes and Forged Videos Using Deep Learning

Johansson, Emil LU (2020) In Master's Theses in Mathematical Sciences FMAM05 20201
Mathematics (Faculty of Engineering)
Abstract
Over just a few years, methods to manipulate videos have become so sophistica- ted that even someone without much expertise or computational resources can forge videos inseparable from pristine ones to the human eye. These methods can for instance insert a person in a video or manipulate their lip movements to make them say anything of the manipulator’s liking. Though there exist harm- less and constructive uses of these technologies, it is not hard to imagine the harm they could cause if put in the wrong hands.
This report presents a model to detect forged manipulated videos, more specifically those where faces have been manipulated. Four kinds of manipu- lation videos were taken into consideration: FaceSwap, DeepFakes, Face2Face and... (More)
Over just a few years, methods to manipulate videos have become so sophistica- ted that even someone without much expertise or computational resources can forge videos inseparable from pristine ones to the human eye. These methods can for instance insert a person in a video or manipulate their lip movements to make them say anything of the manipulator’s liking. Though there exist harm- less and constructive uses of these technologies, it is not hard to imagine the harm they could cause if put in the wrong hands.
This report presents a model to detect forged manipulated videos, more specifically those where faces have been manipulated. Four kinds of manipu- lation videos were taken into consideration: FaceSwap, DeepFakes, Face2Face and Neural Textures. The model proposed consists of a feature extraction CNN followed by an LSTM network. The FaceForensics++ dataset was used, as well as the associated benchmark. The model, though not competing with the state- of-the-art detectors, was able to classify videos with an accuracy higher than or close to that of several models in the benchmark. (Less)
Please use this url to cite or link to this publication:
author
Johansson, Emil LU
supervisor
organization
alternative title
Detektering av Deepfakes och förfalskade videor med hjälp av djupinlärning
course
FMAM05 20201
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Image Analysis, Deepfake, Deep Learning, Neural Networks, FaceSwap, Face2Face, Neural Textures
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3414-2020
ISSN
1404-6342
other publication id
2020:E34
language
English
id
9019746
date added to LUP
2020-06-23 13:17:38
date last changed
2020-06-23 13:17:38
@misc{9019746,
  abstract     = {{Over just a few years, methods to manipulate videos have become so sophistica- ted that even someone without much expertise or computational resources can forge videos inseparable from pristine ones to the human eye. These methods can for instance insert a person in a video or manipulate their lip movements to make them say anything of the manipulator’s liking. Though there exist harm- less and constructive uses of these technologies, it is not hard to imagine the harm they could cause if put in the wrong hands.
This report presents a model to detect forged manipulated videos, more specifically those where faces have been manipulated. Four kinds of manipu- lation videos were taken into consideration: FaceSwap, DeepFakes, Face2Face and Neural Textures. The model proposed consists of a feature extraction CNN followed by an LSTM network. The FaceForensics++ dataset was used, as well as the associated benchmark. The model, though not competing with the state- of-the-art detectors, was able to classify videos with an accuracy higher than or close to that of several models in the benchmark.}},
  author       = {{Johansson, Emil}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Detecting Deepfakes and Forged Videos Using Deep Learning}},
  year         = {{2020}},
}